mem: use np.full() instead of np.ones() * scalar in YoloX preprocess

CHANGELOG.md

@@ -1,5 +1,12 @@
+## 1.5.4
+
+### Enhancement
+- Use `np.full()` instead of `np.ones() * scalar` in YoloX preprocessing to avoid a redundant temporary array
+
 ## 1.5.3
 
+### Enhancement
+
 - Store routing in LayoutElement
 
 ## 1.5.2

unstructured_inference/__version__.py

@@ -1 +1 @@
-__version__ = "1.5.3"  # pragma: no cover
+__version__ = "1.5.4"  # pragma: no cover

unstructured_inference/models/yolox.py

@@ -150,9 +150,9 @@ def image_processing(
 def preprocess(img, input_size, swap=(2, 0, 1)):
     """Preprocess image data before YoloX inference."""
     if len(img.shape) == 3:
-        padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114
+        padded_img = np.full((input_size[0], input_size[1], 3), 114, dtype=np.uint8)
     else:
-        padded_img = np.ones(input_size, dtype=np.uint8) * 114
+        padded_img = np.full(input_size, 114, dtype=np.uint8)
 
     r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1])
     resized_img = cv2.resize(